Scott Chou

As AI workloads eat up global computing supply, DRAM prices are surging like never before—creating major challenges for teams trying to plan out their data center spend. One of my portfolio companies, Mext, is addressing the largest cost component in the datacenter: server memory (DRAM). From being on the board of Redis for the last twelve years, I’ve learned a lot about the various aspects of this problem and approaches to solving them. Mext found a breakthrough, using new AI techniques, for dramatically reducing the amount of server-DRAM required to run applications, all while maintaining performance. It intelligently manages the server’s memory, keeping hot pages (i.e., those in use by applications) in fast DRAM and offloading cold pages (i.e., those less used) to a much less expensive memory tier (e.g., NVMe Flash). Key to the approach is ensuring cold memory pages that are about to be accessed by an application are back in DRAM before the application needs them or notices that they were gone. This is done without modifications to the application or the OS – so it can run in the cloud or on-premise. Mext thus allows applications to either run using less DRAM or keep their DRAM footprint but do more with it. They're hosting a webinar along with Fred Weber (former CTO of AMD) on Jan 22nd at 10am pacific time. You can register here: https://lnkd.in/gXe9GCeE

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🚨NEW EPISODE: Recorded live at FUTURE TITANS 2026 - Jeff Perry of Carta sat down with the iconic Seth Levine, co-founder of Foundry. Seth has been in venture for 25 years, built Foundry from scratch as an emerging manager himself, and has backed about 50 emerging manager funds through his fund of funds. He has genuinely seen every side of this table. They went deep on building Foundry, why VCs are in the influence business, not the decision business, and why the concentration problem in venture is not only bad for LPs, but also for the innovation ecosystem overall. And why Seth's new book, Capital Evolution, is so important for the future of America. 🎧 Links to listen... Apple: https://lnkd.in/ehQUQ2EM Spotify: https://lnkd.in/eU4FExpg

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Sustained ~252–291 GiB/s of HBM memory throughput on H100s under decode load — essentially hitting the hardware roofline. This matters because HBM throughput, not FLOPs, is the real bottleneck in LLM inference. By keeping memory nearly fully saturated, I’ve unlocked far higher efficiency and throughput than standard engines. The result: 0.9–1.36M tokens/sec with ~0.1 ms first-token latency  #AI #LLM #GPURouter #H100 #Inference #CostEfficiency #Innovation #DeepLearning #AIInfrastructure #HighPerformanceComputing NVIDIA AMD OpenAI Google Shilpa Kolhatkar Keith Strier a16z speedrun

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You can have groundbreaking science, elegant engineering, and even early customer interest. But without this one thing, you don't have an investable business. After two decades running IP strategy projects and now leading UC Berkeley's Deep Tech Innovation Lab while building the Berkeley Gateway Accelerator, I've watched countless brilliant technologies die in the valley between breakthrough and business. The problem isn't what most technical founders think it is. It's not about having better tech. It's not about getting more funding. It's not even about finding product-market fit. The fatal flaw shows up much earlier—and it's almost always the same mistake. In my latest piece, I answer questions from a Taiwanese entrepreneur about what really separates deep tech ventures that scale from those that stall. Including one provocative suggestion for Asian ecosystems that has nothing to do with technology. Read the full conversation, linked below. #DeepTech #Innovation #Startups #IntellectualProperty #VentureCapital

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Honored and excited to partner with Drew Baglino and the incredible team at Heron Power as they re-imagine the electric grid for the modern world. Power in America is not zero sum. We deserve access to cheap and abundant electricity and we should win the AI race. And we must build the capacity to manufacture critical technology here at home. When faced with existential challenges, we deploy technology against them. And we win.

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Camber Creek led the $3.7 million Seed round for TwinKnowledge, which is tackling an important problem in the built environment. Construction projects get delayed and rework is required because of discrepancies across construction documents, submittals, requests for information, design, and other plans. TwinKnowledge’s AI Agents absorb structured and unstructured project data, surfacing and preventing conflicts before the work begins. We’re excited about our partnership with CEO Ivan Panushev and his team. #construction #AEC #AI #technology https://ow.ly/mBYX50VtaVG

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The next era of clean energy isn’t just about generating power—it’s about making distributed systems reliable, investable, and built to scale. That’s why we led Omnidian's $87M funding round. Omnidian provides performance assurance for residential and commercial solar and storage systems, using predictive analytics and expert service to maximize uptime, system life, and returns. As distributed energy becomes a core part of the global grid, long-term performance and trust will define the winners. Omnidian is building the infrastructure to make that possible. Congratulations to Mark Liffmann and the entire team—this is just the beginning.

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Varun Sivaram has a truly unique ability to pull together stakeholders across AI, energy, and policy. This is exactly why Earthshot Ventures invested in Emerald AI - there is a grand bargain to be struck in getting data centers online fast if they're willing to bring flexibility to the grid, and Varun is the man to strike that bargain. The latest illustration - bringing together $5T in market cap consisting of the world's leading chip company and the 5 leading IPPs in the US to make this happen!

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I’m thrilled to share that White Star Capital is backing AMI - Advanced Machine Intelligence in their historic $1.03bn seed round 🚀 - Led by the legendary Yann LeCun and Alex LeBrun. AMI isn't building another LLM, they're building World Models. Why this matters: *Beyond Text: Grounded in JEPA architecture to solve physical-world problems. *Global Powerhouse: A dream team from Meta FAIR, OpenAI, and DeepMind. *Sovereign Tech: A credible European-led alternative to US Big Tech. Excited to be part of this journey along with the rest of the WSC team Eric Martineau-Fortin, Matthieu Lattes, Bérénice Moustial and Victoria Pozzi Rocco Belforti. 🌍✨ https://lnkd.in/eiCKZKSQ #VentureCapital #AI

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"Build the event you wish existed" That's what we at Monadical did last week at #NYTW. We wanted an AI Engineers discussion for Engineers. There's always a lot of events out there for VCs to network, or for startups to learn about G2M, but very little on lessons learned from actual engineers in the field. So that's the event we hosted. Big thank yous to our awesome panel, Roy Pereira, Ben Cohen and Corey J. Gallon. Here's the key takeaways and the AI tools we're using. 🚀 All three panelists independently called AI Agents the most transformative LLM application they’ve used. They specifically called out Claude 3.5 Sonnet for its accuracy and reliability. 🪨 We dug into how LLMs are “jagged”, not general. They can be shockingly good at some tasks and completely fail at others. Everyone agreed: good evaluations are critical (and hard.) 🧪 Corey noted how public benchmarks and reality are two different things. Most public evals are saturated or gamed. ♊ Ben emphasized that AI projects are actually two projects: building the tool and building the evaluation process. 🧱 We explored how falling dev costs may impact startup defensibility and labor demand. Roy shared that founders are already shifting strategies in response. ⚒️ In a world of daily AI launches, the panel discussed how they decide what’s worth attention, and what’s just noise. They called out tools like Goose, Aider, Claude Code, and Monadical’s own Cubbi, which helps run agentic workflows safely in dev environments. (links in the comments). CTA: What would you want to hear in an AI Applied Engineering talk you attended?

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I’ve known David Meister for years and backed him at Sydecar, so I’ve seen his ability to build through complexity. Axiom Trust is taking on one of the most outdated parts of wealth infrastructure, trust administration, by pairing a regulated trust company with AI-native workflows. We're proud to invest again and congrats to the team on the launch!

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Great summary of why MaiaEdge.io matters. The AI era is creating massive demand for private, low-latency connectivity across distributed infrastructure. But today’s network interconnection model — built on manual processes and spreadsheet negotiations — simply can’t keep up. MaiaEdge is fixing this. Their platform lets network operators automate private path provisioning, federate with partners, and activate services in minutes instead of months. Founded by Abilash Menon, Tim Ziemer, Patrick Melampey and Andy Ory. This team has been here before. They know how to build carrier-grade infrastructure that operators trust. Congratulations to the entire MaiaEdge team. We’re proud to be part of this journey. #venturecapital #AI #networking #infrastructure #G20Ventures

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Voice is a super interesting modality right now - maybe the first modality we're seeing move to open source models across a number of scale ups / enterprises. Reliability concerns, high costs, and open source model performance are pushing engineers to do their own fine tuning vs. relying on third-party vendors of proprietary models. Many of these orgs have already been collecting their own first-party data and now with third-party vendors like Extrian, David AI, etc they can train really high quality models. RL has been insanely hyped, but it's been unclear how long it will take scale ups and enterprises to actually lean in. Voice AI might be hitting that inflection point faster than expected.

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At CRV, we’ve been fortunate to partner with some of the most visionary founders reshaping the future of cybersecurity over the past five decades. From cloud-native data protection to developer-first security and AI-driven detection, our portfolio reflects the breadth and depth of innovation happening in the industry today. Companies like Cyera, Cribl, Astrix Security, Tailscale, and Vega (among many others) are tackling the hardest problems facing CISOs and developers — from securing non-human identities, to modernizing log pipelines, to making zero-trust networking effortless. What’s exciting is not just the individual companies, but the ecosystem effect: each is building in complementary areas, and together they’re advancing how enterprises secure data, infrastructure, and applications in an AI-powered world. Cybersecurity has never been more mission-critical, and the next decade will see even greater disruption as AI, automation, and new architectures redefine what’s possible. We’re proud to support founders leading that charge.🚀 If you’re building in cyber, we’d love to connect!

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Excited to finally share what we’ve been building over the past year. Today we’re announcing the first investment from ARKITEKT EQUITY. Over the past year, we have been closely observing the structural shifts occurring within semiconductor ecosystems. As manufacturing systems become more complex, the infrastructure enabling those systems becomes increasingly strategic. These environments demand operational clarity, coordination, and long-term alignment. ARKITEKT was built around a simple idea: durable value is created where frontier technology meets pragmatic execution. Our investment in BBTech reflects this conviction. This is just the beginning of what we aim to build at ARKITEKT EQUITY. Stay tuned! https://lnkd.in/g_pQiBZY

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GPUs have gotten faster, and models have gotten larger. But storage is still stuck in the past. Modern AI workloads need instant access to huge datasets. Instead, teams waste time on slow retrieval, idle GPUs, and costly pipelines. Archil is changing this. It delivers object-storage scalability at block-storage speeds. The platform allows for seamless, high-performance access to massive datasets. By eliminating cold starts and accelerating throughput 30X, Archil enables teams to fully utilize their infrastructure and move faster on training, analytics, and AI deployment. Archil’s founder Hunter Leath brings rare technical depth and operating experience from building Amazon EFS and optimizing Netflix’s cloud performance. This is a team building the foundational layer that AI workloads desperately need. I’m thrilled to lead Archil’s seed at Felicis with Nancy Wang and support their mission to reinvent cloud storage for the AI era. https://lnkd.in/gWueXhzz

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Chatting with ZEDEDA CEO, Said Ouissal, and he has an elegant way of explaining the evolution of industrial Edge. Zededa provides OS for orchestrating Edge AI. 2010s were about IoT/Edge Connectivity. 2020s were about Edge computing/processing. 2030s will be all about Edge AI.

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We are thrilled to be part of the Syntracts journey with Douglas Bemis, Christopher Martin! Syntracts is solving the crucial industry paradox of how enterprises can harness the "magic of AI" without compromising data security. We are deeply impressed by their fundamentally different and innovative approach: building secure on-prem legal AI infrastructure at scale. Syntracts utilizes thousands of experts—small language models fine-tuned with synthetic data—to provide reliable, accurate, structured data for complex legal documents. This is the purpose-built, privacy-first data layer the legal industry desperately needs. We are excited to support Syntracts as they scale trust and performance in enterprise AI. Top Harvest Capital is pleased to join Hyperplane, Myriad Venture Partners, Khosla Ventures, Fortitude Ventures, and Point72 Ventures and other respected firms in backing Syntracts. Adam Ghobarah M Waleed Kadous Top Harvest Capital

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